铁道科学与工程学报2025，Vol.22Issue(2)：469-484,16.DOI:10.19713/j.cnki.43-1423/u.T20240651
基于经验约束神经网络的高速铁路路基累积变形预测研究
Research on subgrade cumulative deformation prediction of high-speed railway based on empiricism-constrained neural network
摘要
Abstract
Understanding the deformation evolution of high-speed railway subgrade holds significant importance for controlling deformation issues and managing operations effectively.Currently,machine learning methods are widely used for cumulative deformation prediction,while the traditional machine learning prediction models for cumulative subgrade deformation have the disadvantage of poor out-of-distribution generalization.Hence,a method for predicting cumulative subgrade deformation of high-speed railways based on empiricism-constrained neural networks(ECNN)was proposed.First,the cumulative subgrade deformation prediction dataset was constructed based on engineering field or laboratory test data and divided into training and test sets.Second,a neural network model was built based on the training set,and the optimal neural network prediction model was determined by combining the results of two levels of prediction accuracy and error and prediction uncertainty on the test set.Finally,the optimal neural network model was used to drive the data information of the cumulative subgrade deformation and embed the cumulative plastic strain relationship curve(empirical information)with loss function correction to realize the constraints on the parameters of the optimal neural network model and the loss function.This process completed the model construction of ECNN.The case study shows that the Bidirectional Gate Recurrent Unit(Bi-GRU)model is the optimal neural network model for high-speed railways,with the goodness-of-fit R2 reaching 0.972 59,and the extended uncertainty U95 and the standardized mean deviation fsmd being only 0.015 6 and 0.181 09,respectively.The ECNN model is better in both prediction accuracy and error,and prediction uncertainty levels are comparable with the Bi-GRU model,indicating that the ECNN model considering the constraints of empirical information has stronger prediction performance.The ECNN model has excellent out-of-time-distribution generalization performance as compared to the Bi-GRU model,which can effectively improve the prediction accuracy of cumulative deformation when the training set covers a small time span.The research results can provide a new reference for the prediction of cumulative subgrade deformation of high-speed railway.关键词
高速铁路/累积变形预测/经验约束神经网络/神经网络/累积塑性应变关系Key words
high-speed railway/cumulative deformation prediction/empiricism-constrained neural network/neural network/cumulative plastic strain relationship分类
交通工程引用本文复制引用
邓志兴,徐林荣,李永威,王武斌,苏谦..基于经验约束神经网络的高速铁路路基累积变形预测研究[J].铁道科学与工程学报,2025,22(2):469-484,16.基金项目
铁路基础研究联合基金资助项目(U2268213) （U2268213）
中国中铁股份有限公司科技研究开发计划(2023-重点-09) （2023-重点-09）
国家自然科学基金资助项目(42172322) （42172322）
陆地交通地质灾害防治技术国家工程研究中心团队建设项目(A0920502052401-452) （A0920502052401-452）
